پیش بینی ژن‏ های بیماری با استفاده از دسته‏ بند تک‌کلاسی ماشین بردار پشتیبان

Abstract: In disease gene identification and classification, users are only interested in classifying one specific class, disease genes, without considering other classes (non-disease genes). This situation is referred to as one-class classification. Existing machine learning-based methods typically use known disease gene as positive training set and unknown genes as negative training set to build a classifier. Since there are not any non-disease gene set, in this paper we apply OCSVM (one-class support vector machines) method for one-class classification of genes to identify disease genes. Our experimental results show the superiority of our proposed method in terms of better precision, recall, and F1-measures than existing methods.